An Optimal Transformation for Discriminant and Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables
IEEE Transactions on Information Technology in Biomedicine
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Surface electromyography (EMG) signals in myoelectric control of a motorized prosthesis are recorded commonly using the Ag/AgCl gel electrodes. Although a gelled electrode may provide high-quality EMG recordings, it is inconvenient in clinical application of a myoelectric prosthesis. A novel type of signal sensors, textile electrode, will be ideal for EMG signal recordings in control of a multifunction myoelectric prosthesis. However, it is unknown whether the performance of textile electrodes in control of myoelectric prostheses is comparable to commonly used electrodes. In this study, we used the commercial conductive woven to make textile electrodes for EMG signal recordings and investigated the performance of EMG signals in identifying nine arm and hand movements. Our results in four able-bodied subjects showed that the average classification accuracy across four subjects was 94.34% when using textile electrodes, which was almost same as that of 94.25% when using conventional electrodes. These pilot results suggest that the textile electrodes could achieve similar performance in classifying different arm movements for control of multifunction myoelectric prostheses as conventional gelled electrodes.